Due to the severe interference of background noise, the signal-to-noise ratio of desert seismic data is extremely low. In addition, due to low-frequency characteristics of sand in the Tarim desert region, the background noise in desert seismic data is mainly distributed in low-frequency band, so that the frequency spectrum aliasing of effective signals and background noise is more serious than the general land seismic data. Thus, conventional filtering methods cannot effectively suppress background noise in desert seismic data and recover effective signals. In order to overcome the problem that low-frequency background noise in desert seismic data is hard to suppress, a new method called R-TNRD based on robust principal component analysis (RPCA) algorithm and trainable nonlinear reaction diffusion (TNRD) network is proposed in this paper. By using the good sparsity of RPCA, the input noisy desert seismic data are decomposed into a low-rank matrix and a sparse matrix, and these two matrices contain background noise and effective signals. Due to the serious spectrum aliasing of desert seismic data, conventional thresholds have been unable to extract effective signals from the two matrices obtained by RPCA effectively. Therefore, we introduce TNRD network into desert seismic data denoising. By network training with a low-frequency noise set, the optimisation of TNRD network can be achieved, so as to accurately extract the effective signals from the low-rank matrix and the sparse matrix. In the experimental part, we test the performance of R-TNRD on both synthetic and real seismic data. The results demonstrate that the proposed method can suppress background noise more effectively than conventional methods.

中文翻译：

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使用稳健主成分分析辅助的可训练非线性反应扩散抑制背景噪声

由于背景噪声的严重干扰，沙漠地震资料的信噪比极低。此外，由于塔里木沙漠地区沙子的低频特性，沙漠地震资料中的背景噪声主要分布在低频段，使得有效信号与背景噪声的频谱混叠比地震资料的频谱混叠更为严重。一般陆地地震数据。因此，传统的滤波方法无法有效抑制沙漠地震数据中的背景噪声，恢复有效信号。为了克服沙漠地震数据中低频背景噪声难以抑制的问题，提出了一种基于鲁棒主成分分析（RPCA）算法和可训练非线性反应扩散（TNRD）网络的R-TNRD新方法。这篇报告。利用RPCA良好的稀疏性，将输入的含噪沙漠地震数据分解为低秩矩阵和稀疏矩阵，这两个矩阵包含背景噪声和有效信号。由于沙漠地震数据存在严重的频谱混叠，常规阈值已经无法有效地从RPCA获得的两个矩阵中提取有效信号。因此，我们将TNRD网络引入到沙漠地震数据去噪中。通过使用低频噪声集进行网络训练，可以实现TNRD网络的优化，从而准确地从低秩矩阵和稀疏矩阵中提取有效信号。在实验部分，我们测试了 R-TNRD 在合成和真实地震数据上的性能。